Research on Insurance Decision-Making Model for Extreme Weather Based on ARIMA Algorithms

In the context of escalating global climate change, extreme weather events pose significant challenges to the insurance industry, necessitating robust decision-making models for risk assessment and policy underwriting. This study introduces an innovative insurance decision-making model leveraging the Autoregressive Integrated Moving Average (ARIMA) time series forecasting method to predict extreme weather occurrences and inform insurance policy decisions. The model’s design incorporates a set of carefully selected meteorological indicators, enabling a nuanced analysis of the probability of extreme weather events in specific regions. By applying ARIMA to historical weather data from Zhengzhou and Basel, we demonstrate the model’s efficacy in estimating the required policy volume to achieve a break-even point, thus providing insurers with a data-driven strategy for risk mitigation. The model’s strengths lie in its adaptability to various climatic conditions, its ability to integrate complex time-dependent data, and its potential to enhance the insurance industry’s resilience against the financial impacts of extreme weather. The study underscores the model’s practical utility in optimizing insurance portfolios and contributing to the sustainable development of the insurance sector in the face of climate-related risks.